Laura Pollacci

Laura Pollacci is a postdoctoral researcher at the Knowledge Discovery and Data Mining Laboratory (KDDLab), a joint research group of the Information Science and Technology Institute of the National Research Council (CNR) in Pisa and the University of Pisa, where she holds a position as Research Fellow in the Department of Computer Science.

Her educational background includes:

• Bachelor's Degree in Digital Humanities (2014) from University of Pisa with 110/110 cum laude
• Master's Degree in Digital Humanities (2015) from University of Pisa with 110/110 cum laude
• PhD in Computer Science (2019) from University of Pisa with thesis on 'conjunct usage of Big Data and Sentiment Analysis for the study of Human Migration'

Pollacci's research focuses on Social Network Analysis and Visual Analytics for Social Mining, Human Migration, and Sentiment Analysis. Her work bridges computational methods with social science questions, particularly examining how digital traces can reveal patterns in human behavior, migration, and emotional expression across social platforms. She has developed expertise in analyzing complex social networks, music diversity, and political polarization through data-driven approaches.

Her publication record shows a clear trajectory from computational linguistics and sentiment analysis toward broader applications in social network analysis, migration studies, and musicology. Early work focused on emotive lexicon development and sentiment polarity classification for Italian language, while more recent publications examine political polarization on Reddit, human migration patterns through big data, and the fractal dimensions of music diversity.

Pollacci is actively involved with the HumMingBird project, which focuses on 'Enhanced migration measures from a multidimensional perspective.' Her work at KDDLab places her at the intersection of data science and social research, contributing to the laboratory's mission of developing theory, techniques and systems for extracting knowledge from large data sets.